Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting

To address the nonlinear and non-stationary characteristics of financial time series such as foreign exchange rates, this study proposes a hybrid forecasting model using empirical mode decomposition (EMD) and least squares support vector regression (LSSVR) for foreign exchange rate forecasting. EMD is used to decompose the dynamics of foreign exchange rate into several intrinsic mode function (IMF) components and one residual component. LSSVR is constructed to forecast these IMFs and residual value individually, and then all these forecasted values are aggregated to produce the final forecasted value for foreign exchange rates. Empirical results show that the proposed EMD-LSSVR model outperforms the EMD-ARIMA (autoregressive integrated moving average) as well as the LSSVR and ARIMA models without time series decomposition.     

Professional forecasts of interest rates and exchange rates: Evidence from the Wall Street Journal’s panel of economists

We analyze economists’ forecasts of interest rates and exchange rates from the Wall Street Journal. We find that a majority of economists produced unbiased forecasts but that none predicted directions of changes more accurately than chance. Most economists’ forecast accuracy is statistically indistinguishable from a random walk model in forecasting the Treasury bill rate, but many are significantly worse in forecasting the Treasury bond rate and the exchange rate. We also find systematic forecast heterogeneity, support for strategic models predicting the industry employing the economist matters, and evidence that economists deviate less from the consensus as they age.     

Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

Balassa-Samuelson Theory and Predictability of the US/UK Real Exchange Rate

This paper performs a theory-based forecast of the US/UK real exchange rate. The theory is the Balassa-Samuelson hypothesis that productivity differentials between two countries would determine long-run movements of real exchange rates. The relative income and real exchange rate set a bivariate system, which considers the heteroskedasticity in the real exchange rate movements. The model, to which the Kalman filter and Markov-switching algorithm are applied, is compared with the random walk model and reports significant improvements in forecasting in the medium and long term. [C53, F31]     

Dissecting models' forecasting performance

The fact that the predictive performance of models used in forecasting stock returns, exchange rates, and macroeconomic variables is not stable and varies over time has been widely documented in the forecasting literature. Under these circumstances excessive reliance on forecast evaluation metrics that ignores this instability in forecasting accuracy, like squared errors averaged over the whole forecast evaluation sample, masks important information regarding the temporal evolution of relative forecasting performance of competing models. In this paper we suggest an approach based on the combination of the Cumulated Sum of Squared Forecast Error Differential (CSSFED) of Welch and Goyal (2008) and the Bayesian change point analysis of Barry and Hartigan (1993) that tracks the contribution of forecast errors to the aggregate measures of forecast accuracy observation by observation. In doing so, it allows one to track the evolution of the relative forecasting performance over time. We illustrate the suggested approach by using forecasts of the GDP growth rate in Switzerland.     

UNOFFICIAL EXCHANGE RATES IN BANGLADESH: A COINTEGRATION ANALYSIS

This article estimates a structural model for unofficial market foreign exchange (forex) rates (E U ) and examines the stability of the forex market in Bangladesh using an autoregressive distributed lag approach to cointegration analysis on quarterly data from 1976Q2–1995Q2. It also compares the in-sample and out-of-sample (from 1995Q3–1999Q2) forecasting performances of the structural model with other timE-series models. With E U as a dependent variable and official exchange rates (E O ) , money supply (M) , the difference between foreign and domestic interest rates (I) , forex reserves relative to imports (Q) and political along with some structural factors (D85) as explanatory variables, a multivariate cointegrated relationship is found in which E O , Q , and I cause an appreciation and M and D85 cause a depreciation in E U . These results imply that the overvaluation of the official exchange rate, increases in money supply, the paucity of official forex reserves, and structural factors are the main causes for the creation of the unofficial market for forex in Bangladesh. Results also reveal that the forex market in Bangladesh is stable during the sample period. The structural model performs well in in-sample prediction, and the random walk model performs best in out-of-sample forecasting.     

A heterogeneous agent exchange rate model with speculators and non-speculators

This paper constructs a heterogeneous agent exchange rate model of speculators and non-speculators from a simple monetary framework. The model replaces rational expectations with an adaptive learning rule that forecasts future exchange rates with an econometric model, and assumes two types of market participants, speculators and non-speculators, that differ by their forecasting model. Speculators employ a correctly specified forecasting model, are relatively short-term oriented, and are subject to momentum and herding effects via an expectation shock; non-speculators utilize a simple forecasting model, have no incentive to be short-term oriented, and are not subject to herding effects. Parameters are calibrated and estimated using the method of simulated moments, and simulation results show that the model is able to replicate foreign exchange market stylized facts better than a model of representative agent rational expectations. Furthermore, the dynamics of the model are shown to derive from both agent heterogeneity and the expectation shock.     

A novel time-series model based on empirical mode decomposition for forecasting TAIEX

Stock price prediction is regarded as a challenging task of the financial time series prediction process. Time series models have successfully solved prediction problems in many domains, including the stock market. Unfortunately, there are two major drawbacks in stock market by time-series model: (1) some models cannot be applied to the datasets that do not follow the statistical assumptions; and (2) most time-series models which use stock data with many noises involutedly (caused by changes in market conditions and environments) would reduce the forecasting performance. For solving the above problems and promoting the forecasting performance of time-series models, this paper proposes a hybrid time-series support vector regression (SVR) model based on empirical mode decomposition (EMD) to forecast stock price for Taiwan stock exchange capitalization weighted stock index (TAIEX). In order to evaluate the forecasting performances, the proposed model is compared with autoregressive (AR) model and SVR model. The experimental results show that the proposed model is superior to the listing models in terms of root mean squared error (RMSE). And the more fluctuation year (2000–2001) occurs, the better accuracy of proposed model will be obtained.     

Foreign equity option pricing under stochastic volatility model with double jumps

This paper considers the challenging problem advocated by Huang and Hung (2005), that is to incorporate the stochastic volatility into the foreign equity option pricing. Foreign equity options (quanto options) are contingent claims where the payoff is determined by an equity in one currency but the actual payoff is done in another currency. Huang and Hung (2005) priced foreign equity options under the Lévy processes. In Huang and Hung's paper, they considered jumps in the foreign asset prices and exchange rates and assumed the volatility as constant. However, many studies showed that constant volatility and jumps in returns are incapable of fully capturing the empirical features of equity returns or option prices. In this paper, the stochastic volatility with simultaneous jumps in prices and volatility is proposed to model foreign asset prices and exchange rates. The foreign equity option pricing formula is given by using the Fourier inverse transformation. The numerical results show that the use of stochastic volatility with simultaneous jumps in prices and volatility proposed to model foreign asset prices and exchange rates is necessary and this approach can help us to capture more accurately the foreign equity option prices.     

Forecasting exchange rates using panel model and model averaging

We propose to produce accurate point and interval forecasts of exchange rates by combining a number of well known fundamental based panel models. Combination of each model utilizes a set of weights computed using a linear mixture of experts's framework, where weights are determined by log scores assigned to each model's predictive performance. As well as model uncertainty, we take potential structural break in the parameters of the models into consideration. In our application, to quarterly data for ten currencies (including the Euro) for the period 1990q1–2008q4, we show that the forecasts from ensemble models produce mean and interval forecasts that outperform equal weight, and to a lesser extent random walk benchmark models. The gain from combining forecasts is particularly pronounced for longer-horizon forecasts for central forecasts, but much less so for interval forecasts. Calculations of the probability of the exchange rate rising or falling using the combined or ensemble model show a good correspondence with known events and potentially provide a useful measure for uncertainty of whether the exchange rate is likely to rise or fall.     

Volatility forecasting: combinations of realized volatility measures and forecasting models

This article examines financial time series volatility forecasting performance. Different from other studies which either focus on combining individual realized measures or combining forecasting models, we consider both. Specifically, we construct nine important individual realized measures and consider combinations including the mean, the median and the geometric means as well as an optimal combination. We also apply a simple AR(1) model, an SV model with contemporaneous dependence, an HAR model and three linear combinations of these models. Using the robust forecasting evaluation measures including RMSE and QLIKE, our empirical evidence from both equity market indices and exchange rates suggests that combinations of both volatility measures and forecasting models improve the forecast performance significantly.     

Election Surprises and Exchange Rate Uncertainty

This papers shows that unexpected election results explain some of the unexpected variation in foreign exchange rates. The result is based on an event study which examines the behavior of the size of forecast errors implied by futures contracts for exchange rates around elections. Though elections can produce large unexpected effects on exchange rates, the effects on forecast errors are short-lived. [ JEL codes: D72, F31.]     

Asymmetric loss and herding behaviour of exchange rate forecasters: evidence from South Africa

This study examines the asymmetry of the loss function for private forecasters in exchange rate forecasts of the South African rand. It tests rationality under the possibility of an asymmetric loss function. The results indicate less evidence of asymmetry for a horizon of 1 month but considerable evidence of asymmetry for a horizon of 3 months. However, the shapes of the distributions formed by estimated asymmetry parameters of sub-samples for each forecaster are symmetric, regardless of the forecast horizons, which implies that these forecasters do not herd or antiherd. In fact, the results of our empirical herding test show that forecasters neither herd nor antiherd, which is in sharp contrast to recent findings on antiherding for foreign exchange rates in emerging market economies. Our findings provide consistent evidence for a recent suggestion that antiherding might result in the rejection of rationality, even under asymmetric loss functions. Our findings also suggest that central bank transparency might be associated with herding behaviours.     

D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy

This work proposes a new forecasting model to analyse the economic development of Sichuan province of China. The model, which introduces the concept of diversity, is based on an improvement of the -GMDH algorithm. The new method, called D-GMDH, is compared with two ensemble approaches which are introduced by Dutta (2009), and D-GMDH is better than the two approaches in forecasting accuracy. D-GMDH is also applied to forecast the industrial added value of the Sichuan province. The obtained results are compared with those of the traditional GMDH model, GMDH combination model and the widely used ARMA model. The results show that D-GMDH has good prediction accuracy and is an effective means for economic forecasting when data is contaminated by noise.     

Foreign Direct Investment in Australia Following the Australia–US Free Trade Agreement

A model of inward foreign direct investment for Australia is estimated. Foreign direct investment is found to be positively related to economic and productivity growth and negatively related to foreign portfolio investment, trade openness, the exchange rate and the foreign real interest rate. Foreign direct investment is found to be a substitute for both portfolio investment and trade in goods and services. The exchange rate and the US bond rate affect foreign direct investment through the relative attractiveness of domestic assets. Actual foreign direct investment outperforms a model‐derived forecast in recent years, consistent with the liberalisation of foreign investment screening rules following the Australia–US Free Trade Agreement.     

Combination forecasts of tourism demand with machine learning models

The main objective of this study is to analyse whether the combination of regional predictions generated with machine learning (ML) models leads to improved forecast accuracy. With this aim, we construct one set of forecasts by estimating models on the aggregate series, another set by using the same models to forecast the individual series prior to aggregation, and then we compare the accuracy of both approaches. We use three ML techniques: support vector regression, Gaussian process regression and neural network models. We use an autoregressive moving average model as a benchmark. We find that ML methods improve their forecasting performance with respect to the benchmark as forecast horizons increase, suggesting the suitability of these techniques for mid- and long-term forecasting. In spite of the fact that the disaggregated approach yields more accurate predictions, the improvement over the benchmark occurs for shorter forecast horizons with the direct approach.     

Forecasting and combining competing models of exchange rate determination

This article investigates the out-of-sample forecast performance of a set of competing models of exchange rate determination. We compare standard linear models with models that characterize the relationship between exchange rate and the underlying fundamentals by nonlinear dynamics. Linear models tend to outperform at short forecast horizons especially when deviations from long-term equilibrium are small. In contrast, nonlinear models with more elaborate mean-reverting components dominate at longer horizons especially when deviations from long-term equilibrium are large. The results also suggest that combining different forecasting procedures generally produces more accurate forecasts than can be attained from a single model.     

Black market exchange rates in India: an empirical analysis

In this paper, we explore the determinants of black market (BM) exchange rates in India using annual data from 1955–1994 and integration and cointegration analysis. Two important factors, namely the import capacity of official foreign exchange reserves and restrictions on international trade, have largely been ignored as determinants of BM rates. We stress the importance of these two factors and incorporate them, with others more familiar in the literature, in our theoretical and empirical model for BM rates in India. Our empirical findings show that a low level of official foreign exchange reserves negatively and a high level of trade restrictions positively affect BM rates. We show that the flexible Bretton Woods exchange rate policies for India in 1973 have a negative impact on BM rates. The results also reveal that interest rate policies positively affect BM rates. Thus, our empirical model lends support to the trade and monetary approaches to BM rates and hence, trade restrictions with excess money supply should be removed to eliminate the BMs for forex in India. First Version Received: September 98/Final Version Received: January 2000     

Forecaster Rationality and Expectation Formation in Foreign Exchange Markets: Do Emerging Countries Differ from Industrialized Economies?

ABSTRACT

This paper uses the Consensus Economic Forecast poll to investigate how forecasters in the foreign exchange market form expectations and whether the expectation formation process differs between industrialized and emerging countries. In order to explain the expectation formation of forecasters in countries and country groups, we analyze around 50,000 forecasts for 22 OECD member currencies. We find that differences between the way forecasters in industrialized countries and emerging countries form exchange rate expectations. However, we show that one important difference is due to a difference in forecasting behavior of emerging countries. Controlling for this feature lets the forecasting behavior in emerging countries resemble more the ones found for industrialized countries, but not for all forecast horizons.     

中国资本流动风险预警研究

借鉴FR早期风险预警模型的研究方法,建立适用于预测我国资本流动脆弱性风险的预警系统。我国从未发生过货币危机,在风险预警模型建立时,只能使用我国资本流动脆弱性的变化作为被解释变量。通过计量分析发现:外汇储备/GDP、外汇储备/M2、GDP增长率与我国资本流动脆弱性变化相关性最强,应当作为我国当前预防资本流动脆弱性恶化的重点检测指标。